Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka,Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.     

Soil erosion assessment on hillslope of GCE using RUSLE model

A new method for obtaining the C factor (i.e., vegetation cover and management factor) of the RUSLE model is proposed. The method focuses on the derivation of the C factor based on the vegetation density to obtain a more reliable erosion prediction. Soil erosion that occurs on the hillslope along the highway is one of the major problems in Malaysia, which is exposed to a relatively high amount of annual rainfall due to the two different monsoon seasons. As vegetation cover is one of the important factors in the RUSLE model, a new method that accounts for a vegetation density is proposed in this study. A hillslope near the Guthrie Corridor Expressway (GCE), Malaysia, is chosen as an experimental site whereby eight square plots with the size of \(8\times 8\) and \(5\times 5\) m are set up. A vegetation density available on these plots is measured by analyzing the taken image followed by linking the C factor with the measured vegetation density using several established formulas. Finally, erosion prediction is computed based on the RUSLE model in the Geographical Information System (GIS) platform. The C factor obtained by the proposed method is compared with that of the soil erosion guideline Malaysia, thereby predicted erosion is determined by both the C values. Result shows that the C value from the proposed method varies from 0.0162 to 0.125, which is lower compared to the C value from the soil erosion guideline, i.e., 0.8. Meanwhile predicted erosion computed from the proposed C value is between 0.410 and \(3.925\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}\) compared to 9.367 to \(34.496\, \hbox {t ha}^{-1}\,\hbox {yr}^{-1 }\) range based on the C value of 0.8. It can be concluded that the proposed method of obtaining a reasonable C value is acceptable as the computed predicted erosion is found to be classified as a very low zone, i.e. less than \(10\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}\) whereas the predicted erosion based on the guideline has classified the study area as a low zone of erosion, i.e., between 10 and \(50\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}\).     

Importance of a Coastal Artificial Lake in Methylmercury Production: A Case Study of Lake Changpo,Korea

Ocean Science Journal - Lake Changpo (LC) is an artificial freshwater lake surrounded by wetlands and farmlands, inundated with water during the wet season, on the southwest coast of Korea. To...     

An assessment of regional vulnerability of rice to climate change in India

A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (<2.5 %). However, spatial variations exist for the magnitude of the impact, with some regions likely to be affected more than others. Adaptation strategies comprising agronomical management can offset negative impacts in the near future—particularly in rainfed conditions—but in the longer run, developing suitable varieties coupled with improved and efficient crop husbandry will become essential. For irrigated rice crop, genotypic and agronomic improvements will become crucial; while for rainfed conditions, improved management and additional fertilizers will be needed. Basically climate change is likely to exhibit three types of impacts on rice crop: i) regions that are adversely affected by climate change can gain in net productivity with adaptation; ii) regions that are adversely affected will still remain vulnerable despite adaptation gains; and iii) rainfed regions (with currently low rainfall) that are likely to gain due to increase in rainfall can further benefit by adaptation. Regions falling in the vulnerable category even after suggested adaptation to climate change will require more intensive, specific and innovative adaptation options. The present analysis indicates the possibility of substantial improvement in yields with efficient utilization of inputs and adoption of improved varieties.     

Location-specific weather predictions for Sriharikota (13.72°N, 80.22°E) through numerical atmospheric models during satellite launch campaigns

Accurate knowledge of different meteorological parameters over a launch site is very crucial for efficient management of satellite launch operations. Local weather over the Indian satellite launch site located at Sriharikota High Altitude Range (SHAR: 13.72°N, 80.22°E) is very much dependent on the atmospheric circulation prevailing over the Bay of Bengal oceanic region and topography-induced convective activities. With a view to providing severe weather threat prediction in terms of launch commit criteria (LCC), two numerical atmospheric models namely high-resolution regional model (HRM) and advanced regional prediction system (ARPS) are made operational over SHAR in a synoptic and mesoscale domain, respectively. In the present research article, two launch campaigns through Polar Satellite Launch Vehicle (PSLV-C11 and PSLV-C12) when contrasting weather conditions prevailed over the launch site are chosen for demonstration of potential of two models in providing location-specific short-to-medium-range weather predictions meeting the needs of LCC. In the case of PSLV-C11 campaign, when the launch site underwent frequent thundershower-associated rainfall, ARPS model–derived meteorological fields were effectively used in prediction of probability of the wet spells. On the other hand, Bay of Bengal underwent severe cyclonic storm during PSLV-C12 campaign, and its formation was reasonable captured through HRM simulations. It is concluded that a combination of HRM and ARPS provide reliable short-to-medium-range weather prediction over SHAR, which has got profound importance in launch-related activities.     

Deep resistivity sounding studies for probing deep fresh aquifers in the coastal area of Orissa, India

Exploration and exploitation of groundwater in sedimentary areas are reasonably simple. However, the problem of salinity in coastal areas makes the job very difficult, especially when the freshwater aquifers are not extensive and are entrapped between saline aquifers. States along the eastern coast of India, particularly Orissa with respect to the Mahanadi basin, have acute problems with groundwater salinity. It has been possible to locate horizons of fresh groundwater entrapped between deep saline aquifers in the southwestern part of Mahanadi delta, with the help of deep resistivity soundings along the Delang-Puri profile. This finding has been validated through boreholes and checked with electrical logs of this region. Three freshwater aquifers have been detected: one at shallow depth between 20 and 60?m, the second in the depth range of 90??60?m, and the third in the fractured/weathered basement. The second freshwater aquifer has the most potential; it has a thickness range of 20??0?m and it could be exploited to overcome problematic salinity issues. In general, the depth to basement is variable and it increases seaward.     

Variability of Spectral Energy Distribution of Blazar S5 0716+714

The emission from blazars is known to be variable at all wavelengths. The flux variability is often accompanied by spectral changes. Spectral energy distribution (SED) changes must be associated with changes in the spectra of emitting electrons and/or the physical parameters of the jet. Meaningful modeling of blazar broadband spectra is required to understand the extreme conditions within the emission region. Not only is the broadband SED crucial, but also information about its variability is needed to understand how the highest states of emission occur and how they differ from the low states. This may help in discriminating between models. Here we present the results of our SED modeling of the blazar S5 0716+714 during various phases of its activity. The SEDs are classified into different bins depending on the optical brightness state of the source.     

Measurement of soil-gas radon in some areas of northern Rajasthan,India

The health hazards of the radioactive gas radon on general public are well known. In order to understand the level and distribution of ²²²Rn concentrations in soil-gas in Sri Ganganagar district of Rajasthan, a ²²²Rn survey was carried out for the first time using RAD7, an electronic radon detector manufactured by Durridge Company (USA), at different locations covering a total area of 10,978 km ², having a population of approximately 20 lakh. The measurement of ²²²Rn concentration in soil-gas was carried out at four different depths (10, 40, 70, and 100 cm). The radon concentration in soil-gas for 10, 40, 70, and 100 cm depths ranged from 0.09–4.25, 0.15–6.30, 0.50–9.18, and 0.72–10.40 kBq m ^?3, respectively. The minimum value of radon concentration is observed in 33 GB village at 10 cm depth and maximum for Mohanpura village at 100 cm depth. As expected, our data show an increase of soil-gas radon concentration levels with depth. The present results are compared with the available radon data from other studies.